可在素食人群中确定与代谢健康益处相关的饮食特异性多指标标志物

A. Ouřadová, M. Cahova, J. Gojda, A. Naccarati, G. Ferrero, M. Heniková, Tooba Asif
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摘要

:背景和目的:饮食是基本因素之一,它不仅决定代谢健康,还塑造肠道微生物组和血清代谢组(MIME)。植物性饮食具有潜在的健康益处,但其对 MIME 的影响仍有待阐明。我们试图确定能否在不同地区的素食者的 MIME 中发现依赖于饮食的标记物,以解释所观察到的素食对健康的益处。研究方法来自两个地区(捷克共和国和意大利北部)的体重指数相当的健康瘦素食者(100 人)和杂食者(73 人)参加了横断面研究。根据他们的临床特征和血清标志物,我们调查了他们的葡萄糖和脂质代谢情况,并采用综合多组学方法(16S rRNA 测序、代谢组学和脂质组学分析)确定了国家和饮食特异性 MIME 标志物。结果发现与杂食动物相比,捷克和意大利的素食者表现出更有利的脂质特征参数,其特点是血清中的鞘磷脂、神经酰胺、胆固醇酯和含饱和脂肪酸的脂质种类浓度降低。利用机器学习方法,我们能够根据不同的 omics 数据集区分素食者和杂食者,而不考虑原产国。通过结合所有 MIME 特征,我们能够识别出素食者饮食特异性多组学特征,从而高精度地对素食者和杂食者进行分类。大多数素食者特异性变量与有利的脂质和葡萄糖代谢、炎症或体重指数相关。讨论大多数在素食者中下调的 MIME 指标主要与不良健康结果有关,而上调的 MIME 指标则与健康表型和非传染性疾病的低风险有关。我们的研究结果支持将健康的植物性饮食用于治疗代谢紊乱。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
Diet-Specific Multi-Omics Markers Associated with Metabolic Health Benefits Can Be Determined in Vegan Population
: Background and objectives: Diet is one of the fundamental factors that not only determines metabolic health but also shapes the gut microbiome and serum metabolome (MIME). Plant-based diets are associated with potential health benefits, but their effect on MIME remain to be elucidated. We sought to determine whether diet-dependent markers explaining the observed health benefits of a vegan diet could be identified in the MIME of vegans from different geographic regions. Methods: Lean, healthy vegans ( n = 100) and omnivores ( n = 73) with comparable BMI from two geographical regions (Czech Republic, Northern Italy) participated in the cross-sectional study. Based on their clinical characteristics and serum markers, we investigated their glucose and lipid metabolism and used an integrated multi-omics approach (16S rRNA sequencing, metabolomics and lipidomics profiling) to identify country-and diet-specific MIME markers. Results: Czech and Italian vegans exhibited more favorable lipid profile parameters compared to omnivores characterized by decreased serum concentrations of sphingomyelins, ceramides, cholesterol esters, and lipid species containing saturated fatty acid. Using a machine learning approach, we were able to discriminate between vegans and omnivores based on separate omics datasets, regardless of country of origin. By combining all MIME features, we were able to identify a vegan diet-specific multi-omics signature that allows for the classification of vegans and omnivores with high accuracy. Most of the vegan-specific variables were associated with favorable indices of lipid and glucose metabolism, inflammation, or body weight. Discussion: Most of the MIME markers that are down-regulated in vegans are predominantly associated with adverse health outcomes, whereas those that are up-regulated are associated with a healthy phenotype and a low risk of non-communicable diseases. Our findings support the potential use of a healthy plant-based diet in the treatment of metabolic disorders.
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